Supply Chain Risk Management Policies and Procedures
Specification
Establish, document, approve, communicate, apply, evaluate, and maintain policies and procedures for supply chain risk management. Review and update the policies and procedures at least annually or upon significant changes.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning, Team and expertise
Design, Training, Guardrails
Evaluation, Validation/Red Teaming, Re-evaluation
AI applications, AI Services supply chain, Orchestration
Operations, Maintenance, Continuous monitoring
Archiving, Data deletion, Model disposal
Ownership / SSRM
PI
Shared across the supply chain
Shared control ownership refers to responsibilities and activities related to LLM security that are distributed across multiple stakeholders within the AI supply chain, including the Cloud Service Provider (CSP), Model Provider (MP), Orchestrated Service Provider (OSP), Application Provider (AP), and Customer (AIC). These controls require coordinated actions, communication, and governance across all involved parties to ensure their effectiveness.
Model
Owned by the Model Provider (MP)
The model provider (MP) designs, develops, and implements the control as part of their services or products to mitigate security, privacy, or compliance risks associated with the Large Language Model (LLM). Model Providers are entities that develop, train, and distribute foundational and fine-tuned AI models for various applications. They create the underlying AI capabilities that other actors build upon. Model Providers are responsible for model architecture, training methodologies, performance characteristics, and documentation of capabilities and limitations. They operate at the foundation layer of the AI stack and may provide direct API access to their models. Examples: OpenAI (GPT, DALL-E, Whisper), Anthropic(Claude), Google(Gemini), Meta(Llama), as well as any customized model.
Orchestrated
Owned by the Orchestrated Service Provider (OSP)
The Orchestrated Service Provider (OSP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The OSP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the OSP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The OSP is accountable for ensuring that its providers upstream (e.g MPs) implement the control as it relates to the service/product the develop and offered by the OSP. This refers to entities that create the technical building blocks and management tools that enable AI implementation. This can include platforms, frameworks, and tools that facilitate the integration, deployment, and management of AI models within enterprise workflows. These providers focus on model orchestration and offer services like API access, automated scaling, prompt management, workflow automation, monitoring, and governance rather than end-user functionality or raw infrastructure. They help businesses implement AI in a structured and efficient manner. Examples: AWS, Azure, GCP, OpenAI, Anthropic, LangChain (for AI workflow orchestration), Anyscale (Ray for distributed AI workloads), Databricks (MLflow), IBM Watson Orchestrate, and developer platforms like Google AI Studio.
Application
Owned by the Application Provider (AP)
The Application Provider (AP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The AP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the AP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The AP is accountable for carrying out the due diligence on its upstream providers (e.g MPs, Orchestrated Services) to verify that they implement the control as it relates to the service/product develop and offered by the AP. These providers build and offer end-user applications that leverage generative AI models for specific tasks such as content creation, chatbots, code generation, and enterprise automation. These applications are often delivered as software-as-a-service (SaaS) solutions. These providers focus on user interfaces, application logic, domain-specific functionality, and overall user experience rather than underlying model development. Example: OpenAI (GPTs,Assistants), Zapier, CustomGPT, Microsoft Copilot (integrated into Office products), Jasper (AI-driven content generation), Notion AI (AI-enhanced productivity tools), Adobe Firefly (AI-generated media), and AI-powered customer service solutions like Amazon Rufus, as well as any organization that develops its AI-based application internally.
Implementation guidelines
Auditing guidelines
1. Ensure GPU sourcing, model deployment layers, and marketplace vendors are covered. 2. Validate sign-offs by risk/compliance leads. 3. Inspect internal SOPs and published transparency reports, to verify communication to internal and external stakeholders. 4. Review onboarding and vetting workflows for open-source, ML libraries, hardware vendors, to specify the implementation depth. 5. Request sample SBOMs, risk scorecards, or breach history logs, to check the supplier risk evaluation. 6. Verify the most recent update and trigger events for policy review (e.g., breach, new partner). 7. Check that the policy aligns with recognized cloud security and supply chain risk management standards and applicable regulations. 8. Verify that monitoring metrics or internal/external audits are performed periodically to evaluate policy effectiveness.
Standards mappings
42001: D.2 - Integration of AI management system with other management system standards 42001: B.10.3 - Suppliers 27001: A.5.19 - Information security in supplier relationships 27001: A.5.21 - Managing information security in the information and communication technology (ICT) supply chain 27001: A.5.1 - Policies for information security
Addendum
N/A
Article 17 (1) (l) Article 25 Article 53 Article 55 Article 56 Article 72
Addendum
Formal, leadership-backed governance program, and mandate policy review schedules or approval workflows.
GV-4.1-001 GV-4.1-003 GV-6.1-004
Addendum
N/A
C4 PC-02 C5 SSO-01 C5 OIS-03 C5 OIS-04 C5 PSS-01
Addendum
N/A
AI-CAIQ questions (2)
Are policies and procedures for supply chain risk management established, documented, approved, communicated, applied, evaluated, and maintained?
Are the policies and procedures reviewed and updated at least annually or upon significant changes?